Midterm 2 Master Notes
Lejla Becirevic
2024-02-10
getwd()## [1] "/Users/lejla.becirevic/Desktop/BIS15W2024_lbecirevic/midterm_notes/m2"library(tidyverse)
library(janitor)
library(skimr)
library(naniar)
library(visdat)
library(here)
library(palmerpenguins)
library(RColorBrewer)
library(paletteer)options(scipen=999) #cancels the use of scientific notation for the sessionLAB 8
summarize() and group_by() are powerful tools that we can use to produce clean summaries of data. Especially when used together, we can quickly group variables of interest and save time
print(penguins)## # A tibble: 344 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>Produce a summary of the mean for bill_length_mm, bill_depth_mm, flipper_length_mm, and body_mass_g within Adelie penguins only. Be sure to provide the number of samples
penguins %>%
filter(species=="Adelie") %>%
summarize(mean_bill_length=mean(bill_length_mm, na.rm = T),
mean_bill_depth=mean(bill_depth_mm, na.rm = T),
mean_flippper_length=mean(flipper_length_mm, na.rm = T),
mean_body_mass=mean(body_mass_g, na.rm = T),
n=n())## # A tibble: 1 × 5
## mean_bill_length mean_bill_depth mean_flippper_length mean_body_mass n
## <dbl> <dbl> <dbl> <dbl> <int>
## 1 38.8 18.3 190. 3701. 152across()
Function in dplyr called across() which can
count for multiple variables
Use summarize() to produce distinct counts over
multiple variables; i.e. species, island, and sex?
penguins %>%
summarize(distinct_species = n_distinct(species),
distinct_island = n_distinct(island),
distinct_sex = n_distinct(sex))## # A tibble: 1 × 3
## distinct_species distinct_island distinct_sex
## <int> <int> <int>
## 1 3 3 3By using across() we can reduce the clutter and
make things cleaner
penguins %>%
summarize(across(c(species, island, sex), n_distinct))## # A tibble: 1 × 3
## species island sex
## <int> <int> <int>
## 1 3 3 3This is very helpful for continuous variables
penguins %>%
summarize(across(contains("mm"), mean, na.rm=T))## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(contains("mm"), mean, na.rm = T)`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## # A tibble: 1 × 3
## bill_length_mm bill_depth_mm flipper_length_mm
## <dbl> <dbl> <dbl>
## 1 43.9 17.2 201.group_by also works
penguins %>%
group_by(sex) %>%
summarize(across(contains("mm"), mean, na.rm=T))## # A tibble: 3 × 4
## sex bill_length_mm bill_depth_mm flipper_length_mm
## <fct> <dbl> <dbl> <dbl>
## 1 female 42.1 16.4 197.
## 2 male 45.9 17.9 205.
## 3 <NA> 41.3 16.6 199Here we summarize across all variables
penguins %>%
summarise_all(n_distinct)## # A tibble: 1 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <int> <int> <int> <int> <int> <int>
## 1 3 3 165 81 56 95
## # ℹ 2 more variables: sex <int>, year <int>Operators can also work, here I am summarizing n_distinct() across all variables except species, island, and sex
penguins %>%
summarize(across(!c(species, island, sex, year),
mean, na.rm=T))## # A tibble: 1 × 4
## bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <dbl> <dbl> <dbl> <dbl>
## 1 43.9 17.2 201. 4202.All variables that include “bill”…all of the other dplyr operators also work
penguins %>%
summarise(across(starts_with("bill"), n_distinct))## # A tibble: 1 × 2
## bill_length_mm bill_depth_mm
## <int> <int>
## 1 165 81Produce separate summaries of the mean and standard deviation for bill_length_mm, bill_depth_mm, and flipper_length_mm for each penguin species. Be sure to provide the number of samples
penguins %>%
group_by(species) %>%
summarise(across(c(contains("mm"), body_mass_g), mean, na.rm=T), #included variable after contains "mm" with the c() function, can also add stuff after
n=n())## # A tibble: 3 × 6
## species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g n
## <fct> <dbl> <dbl> <dbl> <dbl> <int>
## 1 Adelie 38.8 18.3 190. 3701. 152
## 2 Chinstrap 48.8 18.4 196. 3733. 68
## 3 Gentoo 47.5 15.0 217. 5076. 124getwd()## [1] "/Users/lejla.becirevic/Desktop/BIS15W2024_lbecirevic/midterm_notes/m2"Load the mammals life history data and clean the names
life_history <- read_csv("data/mammal_lifehistories_v3.csv") %>% clean_names()## Rows: 1440 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): order, family, Genus, species, newborn
## dbl (8): mass, gestation, weaning, wean mass, AFR, max. life, litter size, l...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Here is a new one for you using the purrr
package. This will give you a quick summary of the number of NA’s in
each variable.
life_history %>%
map_df(~ sum(is.na(.)))## # A tibble: 1 × 13
## order family genus species mass gestation newborn weaning wean_mass afr
## <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 0 0 0 0 0 0 0 0 0 0
## # ℹ 3 more variables: max_life <int>, litter_size <int>, litters_year <int>A single approach to deal with NA’s in this data set
life_history <- read_csv("data/mammal_lifehistories_v3.csv", na= c("NA", " ", ".", "-999", "not measured")) %>% clean_names() # you nee to know how the NAs are represented in the data; you dont want to do this by default## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 1440 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): order, family, Genus, species
## dbl (9): mass, gestation, newborn, weaning, wean mass, AFR, max. life, litte...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.naniar
naniar is a package that is built to manage NA’s
miss_var_summary provides a clean summary of NA’s across the data frame
naniar::miss_var_summary(life_history)## # A tibble: 13 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 wean_mass 1039 72.2
## 2 litters_year 689 47.8
## 3 weaning 619 43.0
## 4 afr 607 42.2
## 5 newborn 595 41.3
## 6 gestation 418 29.0
## 7 mass 85 5.90
## 8 litter_size 84 5.83
## 9 order 0 0
## 10 family 0 0
## 11 genus 0 0
## 12 species 0 0
## 13 max_life 0 0Let’s use mutate() and na_if() to replace 0’s with NA’s
life_history <-
life_history %>%
mutate(max_life=na_if(max_life, 0))We can also use miss_var_summary with
group_by(). This helps us better evaluate where NA’s are in
the data.
life_history %>%
group_by(order) %>%
select(order, wean_mass) %>%
miss_var_summary(order=T)## # A tibble: 17 × 4
## # Groups: order [17]
## order variable n_miss pct_miss
## <chr> <chr> <int> <dbl>
## 1 Artiodactyla wean_mass 134 83.2
## 2 Carnivora wean_mass 120 60.9
## 3 Cetacea wean_mass 51 92.7
## 4 Dermoptera wean_mass 2 100
## 5 Hyracoidea wean_mass 3 75
## 6 Insectivora wean_mass 67 73.6
## 7 Lagomorpha wean_mass 28 66.7
## 8 Macroscelidea wean_mass 8 80
## 9 Perissodactyla wean_mass 12 80
## 10 Pholidota wean_mass 3 42.9
## 11 Primates wean_mass 108 69.2
## 12 Proboscidea wean_mass 1 50
## 13 Rodentia wean_mass 474 71.3
## 14 Scandentia wean_mass 5 71.4
## 15 Sirenia wean_mass 4 80
## 16 Tubulidentata wean_mass 0 0
## 17 Xenarthra wean_mass 19 95naniar also has a nice replace function which will allow you to precisely control which values you want replaced with NA’s in each variable.
life_history %>%
replace_with_na(replace = list(newborn = "not measured",
weaning= -999,
wean_mass= -999,
afr= -999,
max_life= 0,
litter_size= -999,
gestation= -999,
mass= -999)) %>%
miss_var_summary() #makes replacement of NAs specific with variable## # A tibble: 13 × 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 wean_mass 1039 72.2
## 2 max_life 841 58.4
## 3 litters_year 689 47.8
## 4 weaning 619 43.0
## 5 afr 607 42.2
## 6 newborn 595 41.3
## 7 gestation 418 29.0
## 8 mass 85 5.90
## 9 litter_size 84 5.83
## 10 order 0 0
## 11 family 0 0
## 12 genus 0 0
## 13 species 0 0**Import the data and do a little exploration. Be sure to clean the names if necessary.
cites <- read_csv("data/cites.csv") %>% clean_names()## Rows: 67161 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): App., Taxon, Class, Order, Family, Genus, Importer, Exporter, Orig...
## dbl (3): Year, Importer reported quantity, Exporter reported quantity
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Try using group_by() with naniar.
Look specifically at class and exporter_reported_quantity.
For which taxonomic classes do we have a high proportion of missing
export data?
cites %>%
select(class, exporter_reported_quantity) %>%
group_by(class) %>%
miss_var_summary() %>%
arrange(desc(pct_miss))## # A tibble: 17 × 4
## # Groups: class [17]
## class variable n_miss pct_miss
## <chr> <chr> <int> <dbl>
## 1 Holothuroidea exporter_reported_quantity 10 100
## 2 Dipneusti exporter_reported_quantity 3 75
## 3 Bivalvia exporter_reported_quantity 165 61.3
## 4 Gastropoda exporter_reported_quantity 104 54.5
## 5 Elasmobranchii exporter_reported_quantity 58 51.3
## 6 Arachnida exporter_reported_quantity 32 47.8
## 7 Amphibia exporter_reported_quantity 190 45.2
## 8 Anthozoa exporter_reported_quantity 3858 43.9
## 9 Mammalia exporter_reported_quantity 3731 43.9
## 10 <NA> exporter_reported_quantity 7002 34.6
## 11 Hydrozoa exporter_reported_quantity 61 33.7
## 12 Hirudinoidea exporter_reported_quantity 11 32.4
## 13 Reptilia exporter_reported_quantity 5323 28.9
## 14 Actinopteri exporter_reported_quantity 726 26.3
## 15 Aves exporter_reported_quantity 1792 26.1
## 16 Insecta exporter_reported_quantity 74 23.9
## 17 Coelacanthi exporter_reported_quantity 0 0Visualizing NAs
vis_dat(life_history) #classes of data
vis_miss(life_history)
Dealing with NA’s in advance
If you are sure that you know how NA’s are treated in the
data, then you can deal with them in advance using na() as
part of the readr package.
life_history_advance <-
readr::read_csv(file = "data/mammal_lifehistories_v3.csv",
na = c("NA", " ", ".", "-999")) #all NA, blank spaces, .,and -999 are treated as NA## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 1440 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): order, family, Genus, species, newborn
## dbl (8): mass, gestation, weaning, wean mass, AFR, max. life, litter size, l...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.LAB 9
pivot_longer
Scientists frequently use spreadsheets that are organized to
make data entry efficient. This is often referred to as wide format.
Unfortunately, the wide format creates a problem because column names
may actually represent values of a variable. The command
pivot_longer() shifts data from wide to long
format.
Rules: + pivot_longer(cols, names_to,
values_to) + cols - Columns to pivot to longer format +
names_to - Name of the new column; it will contain the
column names of gathered columns as values + values_to -
Name of the new column; it will contain the data stored in the values of
gathered columns
Assess whether or not the data are tidy.
(1) each variable has its own column?
(2) each observation has its own row? (3) each value has its own
cell?
To fix this problem, we need to reshape the table to long
format while keeping track of column names and values. We do this using
pivot_longer(). Notice that the dimensions of the data
frame change.
heartrate <- read_csv("data/heartrate.csv")## Rows: 6 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): patient
## dbl (4): a, b, c, d
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.heartrate %>%
pivot_longer(-patient, #patient will not move
names_to = "drug", #make a new column called "drug"
values_to="heartrate" #values moved to a new column called "heartrate"
)## # A tibble: 24 × 3
## patient drug heartrate
## <chr> <chr> <dbl>
## 1 Margaret a 72
## 2 Margaret b 74
## 3 Margaret c 80
## 4 Margaret d 68
## 5 Frank a 84
## 6 Frank b 84
## 7 Frank c 88
## 8 Frank d 76
## 9 Hawkeye a 64
## 10 Hawkeye b 66
## # ℹ 14 more rowsExamples Import the file
relig_income.csv and store it as a new object
relig_income.
relig_income <- read_csv("data/relig_income.csv")## Rows: 18 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): religion
## dbl (10): <$10k, $10-20k, $20-30k, $30-40k, $40-50k, $50-75k, $75-100k, $100...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Use pivot_longer() to make the data tidy.
relig_income %>%
pivot_longer(-religion,
names_to = "income",
values_to = "total")## # A tibble: 180 × 3
## religion income total
## <chr> <chr> <dbl>
## 1 Agnostic <$10k 27
## 2 Agnostic $10-20k 34
## 3 Agnostic $20-30k 60
## 4 Agnostic $30-40k 81
## 5 Agnostic $40-50k 76
## 6 Agnostic $50-75k 137
## 7 Agnostic $75-100k 122
## 8 Agnostic $100-150k 109
## 9 Agnostic >150k 84
## 10 Agnostic Don't know/refused 96
## # ℹ 170 more rowsSome (but not all) of the column names are data. We also have NA’s.
billboard <- read_csv("data/billboard.csv")## Rows: 317 Columns: 79
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): artist, track
## dbl (65): wk1, wk2, wk3, wk4, wk5, wk6, wk7, wk8, wk9, wk10, wk11, wk12, wk...
## lgl (11): wk66, wk67, wk68, wk69, wk70, wk71, wk72, wk73, wk74, wk75, wk76
## date (1): date.entered
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Solution 1: specify a range of columns that you want to pivot.
billboard2 <-
billboard %>%
pivot_longer(wk1:wk76, # a range of columns
names_to = "week",
values_to = "rank",
values_drop_na = TRUE #this will drop the NA's
)Solution 2: OR, specify columns that you want to stay fixed.
billboard3 <-
billboard %>%
pivot_longer(-c(artist, track, date.entered), #specific columns to stay fixed
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
)Solution 3: identify columns by a prefix, remove the prefix and all NA’s.
billboard %>%
pivot_longer(
cols = starts_with("wk"), #columns that start with "wk"
names_to = "week",
names_prefix = "wk",
values_to = "rank",
values_drop_na = TRUE)## # A tibble: 5,307 × 5
## artist track date.entered week rank
## <chr> <chr> <date> <chr> <dbl>
## 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
## 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
## 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
## 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
## 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
## 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
## 7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
## 8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
## 9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
## 10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
## # ℹ 5,297 more rowsImport plant_data.csv as a new object
plant_data.
plant_data <- read_csv("data/plant_data.csv")## Rows: 3 Columns: 33
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): genotype, water_sched_prog, greenhouse
## dbl (30): day1, day2, day3, day4, day5, day6, day7, day8, day9, day10, day11...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Use pivot_longer() to make the data tidy. Focus the data
only on genotype, water_sched_prog, and greenhouse.
plant_data %>%
pivot_longer(-c(genotype, water_sched_prog, greenhouse),
names_to = "day",
values_to = "v2",
values_drop_na = TRUE)## # A tibble: 90 × 5
## genotype water_sched_prog greenhouse day v2
## <chr> <chr> <chr> <chr> <dbl>
## 1 control A A761 day1 21.7
## 2 control A A761 day2 19.9
## 3 control A A761 day3 20.7
## 4 control A A761 day4 19.4
## 5 control A A761 day5 20.2
## 6 control A A761 day6 19.2
## 7 control A A761 day7 20.6
## 8 control A A761 day8 19.9
## 9 control A A761 day9 19.2
## 10 control A A761 day10 20.4
## # ℹ 80 more rowsRecall that we use pivot_longer() when our column names
actually represent variables. A classic example would be that the column
names represent observations of a variable.
datasets::USPersonalExpenditure## 1940 1945 1950 1955 1960
## Food and Tobacco 22.200 44.500 59.60 73.2 86.80
## Household Operation 10.500 15.500 29.00 36.5 46.20
## Medical and Health 3.530 5.760 9.71 14.0 21.10
## Personal Care 1.040 1.980 2.45 3.4 5.40
## Private Education 0.341 0.974 1.80 2.6 3.64?USPersonalExpenditureHere we add a new column of expenditure types, which are stored as
rownames above, with mutate(). The
USPersonalExpenditures data also needs to be converted to a
data frame before we can use the tidyverse functions, because it comes
as a matrix.
expenditures <- USPersonalExpenditure %>%
as_tibble() %>% #this transforms the matrix into a data frame
mutate(expenditure = rownames(USPersonalExpenditure))Are these data tidy? Please use pivot_longer() to tidy
the data.
expenditures %>%
pivot_longer(-expenditure, #not moving expenditure
names_to = "year", #make new variable = year
values_to = "bn_dollars") #moving values into bn_dollars## # A tibble: 25 × 3
## expenditure year bn_dollars
## <chr> <chr> <dbl>
## 1 Food and Tobacco 1940 22.2
## 2 Food and Tobacco 1945 44.5
## 3 Food and Tobacco 1950 59.6
## 4 Food and Tobacco 1955 73.2
## 5 Food and Tobacco 1960 86.8
## 6 Household Operation 1940 10.5
## 7 Household Operation 1945 15.5
## 8 Household Operation 1950 29
## 9 Household Operation 1955 36.5
## 10 Household Operation 1960 46.2
## # ℹ 15 more rowsRestrict the data to medical and health expenditures only. Sort in ascending order.
expenditures %>%
pivot_longer(-expenditure,
names_to = "year",
values_to = "bn_dollars") %>%
filter(expenditure=="Medical and Health") %>%
arrange(-bn_dollars)## # A tibble: 5 × 3
## expenditure year bn_dollars
## <chr> <chr> <dbl>
## 1 Medical and Health 1960 21.1
## 2 Medical and Health 1955 14
## 3 Medical and Health 1950 9.71
## 4 Medical and Health 1945 5.76
## 5 Medical and Health 1940 3.53expenditures %>%
pivot_longer(-expenditure,
names_to = "year",
values_to = "bn_dollars") %>%
filter(expenditure=="Private Education") ## # A tibble: 5 × 3
## expenditure year bn_dollars
## <chr> <chr> <dbl>
## 1 Private Education 1940 0.341
## 2 Private Education 1945 0.974
## 3 Private Education 1950 1.8
## 4 Private Education 1955 2.6
## 5 Private Education 1960 3.64names_sep
Helps pull these apart, but we still have “exp” and “rep” to deal with.
qpcr_untidy <- read_csv("data/qpcr_untidy.csv")## Rows: 5 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): gene
## dbl (9): exp1_rep1, exp1_rep2, exp1_rep3, exp2_rep1, exp2_rep2, exp2_rep3, e...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.qpcr_untidy %>%
pivot_longer(
exp1_rep1:exp3_rep3, #range
names_to= c("experiment", "replicate"), #names of columns
names_sep="_", #separate by _
values_to="mRNA_expression")## # A tibble: 45 × 4
## gene experiment replicate mRNA_expression
## <chr> <chr> <chr> <dbl>
## 1 A exp1 rep1 21.7
## 2 A exp1 rep2 19.8
## 3 A exp1 rep3 20.7
## 4 A exp2 rep1 18.3
## 5 A exp2 rep2 20.4
## 6 A exp2 rep3 17.6
## 7 A exp3 rep1 20.6
## 8 A exp3 rep2 19.9
## 9 A exp3 rep3 19.2
## 10 B exp1 rep1 24.3
## # ℹ 35 more rowsseparate
In this new heart rate example, we have the sex of each patient included with their name. Are these data tidy? No, there is more than one value per cell in the patient column and the columns a, b, c, d once again represent values.
heartrate2 <- read_csv("data/heartrate2.csv")## Rows: 6 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): patient
## dbl (4): a, b, c, d
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.heartrate2## # A tibble: 6 × 5
## patient a b c d
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Margaret_f 72 74 80 68
## 2 Frank_m 84 84 88 76
## 3 Hawkeye_m 64 66 68 64
## 4 Trapper_m 60 58 64 58
## 5 Radar_m 74 72 78 70
## 6 Henry_m 88 87 88 72We need to start by separating the patient names from their sexes.
separate() needs to know which column you want to split,
the names of the new columns, and what to look for in terms of breaks in
the data.
heartrate2 %>%
separate(patient, into= c("patient", "sex"), sep = "_") #seperate patient from sex with the _## # A tibble: 6 × 6
## patient sex a b c d
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Margaret f 72 74 80 68
## 2 Frank m 84 84 88 76
## 3 Hawkeye m 64 66 68 64
## 4 Trapper m 60 58 64 58
## 5 Radar m 74 72 78 70
## 6 Henry m 88 87 88 72Examples Re-examine heartrate2. Use
separate() for the sexes, pivot_longer() to
tidy, and arrange() to organize by patient and drug. Store
this as a new object heartrate3.
heartrate3 <- heartrate2 %>%
separate(patient, into=c("patient", "sex"), sep="_") %>%
pivot_longer(-c(patient, sex),
names_to = "drug",
values_to = "heartrate")
heartrate3## # A tibble: 24 × 4
## patient sex drug heartrate
## <chr> <chr> <chr> <dbl>
## 1 Margaret f a 72
## 2 Margaret f b 74
## 3 Margaret f c 80
## 4 Margaret f d 68
## 5 Frank m a 84
## 6 Frank m b 84
## 7 Frank m c 88
## 8 Frank m d 76
## 9 Hawkeye m a 64
## 10 Hawkeye m b 66
## # ℹ 14 more rowsunite
Opposite of separate(). Its syntax is straightforward. You
only need to give a new column name and then list the columns to combine
with a separation character. Give it a try below by recombining patient
and sex from heartrate3.
heartrate3 %>%
unite(patient_sex, "patient", "sex", sep=" ") #does the opposite, takes two variables and combines## # A tibble: 24 × 3
## patient_sex drug heartrate
## <chr> <chr> <dbl>
## 1 Margaret f a 72
## 2 Margaret f b 74
## 3 Margaret f c 80
## 4 Margaret f d 68
## 5 Frank m a 84
## 6 Frank m b 84
## 7 Frank m c 88
## 8 Frank m d 76
## 9 Hawkeye m a 64
## 10 Hawkeye m b 66
## # ℹ 14 more rowspivot_wider
The opposite of pivot_longer(). You use
pivot_wider() when you have an observation scattered across
multiple rows. In the example below, cases and
population represent variable names not observations.
Rules:
+ pivot_wider(names_from, values_from)
+ names_from - Values in the names_from column
will become new column names
+ values_from - Cell values will be taken from the
values_from column
tb_data <- read_csv("data/tb_data.csv") #value column is not represented of single variable ## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, key
## dbl (2): year, value
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.When using pivot_wider() we use names_from
to identify the variables (new column names) and
values_from to identify the values associated with the new
columns.
tb_data %>%
pivot_wider(names_from = "key", #the observations under key will become new columns
values_from = "value") #the values under value will be moved to the new columns## # A tibble: 6 × 4
## country year cases population
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583Examples Load the gene_exp.csv data as
a new object gene_exp. Are these data tidy? Use
pivot_wider() to tidy the data.
gene_exp <- read_csv("data/gene_exp.csv")## Rows: 6 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): gene_id, type
## dbl (1): L4_values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.gene_exp %>%
pivot_wider(names_from = "type",
values_from = "L4_values")## # A tibble: 3 × 3
## gene_id treatment control
## <chr> <dbl> <dbl>
## 1 gene1 15.6 19.3
## 2 gene2 22.2 16.0
## 3 gene3 17.7 26.9Load the beachbugs data and have a look.
beachbugs <- read_csv("data/beachbugs_long.csv")## Rows: 66 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): site
## dbl (2): year, buglevels
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Use pivot_wider to transform the data into wide
format.
beachbugs_wide <- beachbugs %>%
pivot_wider(names_from = site,
values_from = buglevels)Now, use pivot_longer to transform them back to
long!
beachbugs_wide %>%
pivot_longer(-year,
names_to = "site",
values_to = "buglevels") %>%
arrange(desc(buglevels))## # A tibble: 66 × 3
## year site buglevels
## <dbl> <chr> <dbl>
## 1 2013 Little Bay Beach 122.
## 2 2018 South Maroubra Rockpool 112.
## 3 2013 Malabar Beach 101.
## 4 2013 South Maroubra Rockpool 96.4
## 5 2016 Malabar Beach 91.0
## 6 2015 Malabar Beach 66.9
## 7 2016 Bronte Beach 61.3
## 8 2016 Coogee Beach 59.5
## 9 2016 South Maroubra Rockpool 59.3
## 10 2018 Little Bay Beach 59.1
## # ℹ 56 more rowsLAB 10
Grammar of Graphics
The ability to quickly produce and edit graphs and charts is a
strength of R. These data visualizations are produced by the package
ggplot2 and it is a core part of the tidyverse. The syntax
for using ggplot is specific and common to all of the plots. This is
what Hadley Wickham calls a Grammar of
Graphics. The “gg” in ggplot stands for grammar of
graphics.
Philosophy
What makes a good chart? In my opinion a good chart is elegant in its simplicity. It provides a clean, clear visual of the data without being overwhelming to the reader. This can be hard to do and takes some careful thinking. Always keep in mind that the reader will almost never know the data as well as you do so you need to be mindful about presenting the facts.
Data Types
We first need to define some of the data types we will use to build plots.
discretequantitative data that only contains integerscontinuousquantitative data that can take any numerical valuecategoricalqualitative data that can take on a limited number of values
Basics
The syntax used by ggplot takes some practice to get used to, especially for customizing plots, but the basic elements are the same. It is helpful to think of plots as being built up in layers.
In short, plot= data + geom_ + aesthetics.
We start by calling the ggplot function, identifying the data, and
specifying the axes. We then add the geom type to describe
how we want our data represented. Each geom_ works with
specific types of data and R is capable of building plots of single
variables, multiple variables, and even maps. Lastly, we add
aesthetics.
Example To make things easy, let’s start with some built in data.
names(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"glimpse(iris)## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…To make a plot, we need to first specify the data and map the aesthetics. The aesthetics include how each variable in our data set will be used. In the example below, I am using the aes() function to identify the x and y variables in the plot.
ggplot(data=iris, #specify the data
mapping=aes(x=Species, y=Petal.Length)) #map the aesthetics
Notice that we have a nice background, labeled axes, and even a value
range of our variables on the y-axis- but no plot. This is because we
need to tell ggplot how we want our data represented. This is called the
geometry or geom(). There are many types of
geom, see the ggplot cheatsheet.
Here we specify that we want a boxplot, indicated by
geom_boxplot().
ggplot(data=iris, #specify the data
mapping=aes(x=Species, y=Petal.Length))+ #map the aesthetics
geom_boxplot() #add the plot type
Use the iris data to build a scatterplot that compares sepal length
vs. sepal width. Use the cheat sheet for help to find the correct
geom_ for a scatterplot.
names(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"ggplot(data=iris,
mapping = aes(x=Sepal.Length, y=Sepal.Width))+
geom_point()
Scatter Plots
Scatter plots are good at revealing relationships that are not readily visible in the raw data. For now, we will not add regression aka. “best of fit” lines or calculate any r2 values.
In the case below, we are exploring whether or not there is a relationship between animal mass and home range. We are using the log transformed values because there is a large difference in mass and home range among the different species in the data.
Now that we have a general idea of the syntax, let’s start by working with two common plots: 1) scatter plots and 2) bar plots.
homerange <- read_csv("data/Tamburelloetal_HomeRangeDatabase.csv")## Rows: 569 Columns: 24
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): taxon, common.name, class, order, family, genus, species, primarym...
## dbl (8): mean.mass.g, log10.mass, mean.hra.m2, log10.hra, dimension, preyma...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.What is the structure of the homerange data? Does it
have any NA’s? Is it tidy? Do a quick exploratory analysis of your
choice below.
glimpse(homerange)## Rows: 569
## Columns: 24
## $ taxon <chr> "lake fishes", "river fishes", "river fishe…
## $ common.name <chr> "american eel", "blacktail redhorse", "cent…
## $ class <chr> "actinopterygii", "actinopterygii", "actino…
## $ order <chr> "anguilliformes", "cypriniformes", "cyprini…
## $ family <chr> "anguillidae", "catostomidae", "cyprinidae"…
## $ genus <chr> "anguilla", "moxostoma", "campostoma", "cli…
## $ species <chr> "rostrata", "poecilura", "anomalum", "fundu…
## $ primarymethod <chr> "telemetry", "mark-recapture", "mark-recapt…
## $ N <chr> "16", NA, "20", "26", "17", "5", "2", "2", …
## $ mean.mass.g <dbl> 887.00, 562.00, 34.00, 4.00, 4.00, 3525.00,…
## $ log10.mass <dbl> 2.9479236, 2.7497363, 1.5314789, 0.6020600,…
## $ alternative.mass.reference <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ mean.hra.m2 <dbl> 282750.00, 282.10, 116.11, 125.50, 87.10, 3…
## $ log10.hra <dbl> 5.4514026, 2.4504031, 2.0648696, 2.0986437,…
## $ hra.reference <chr> "Minns, C. K. 1995. Allometry of home range…
## $ realm <chr> "aquatic", "aquatic", "aquatic", "aquatic",…
## $ thermoregulation <chr> "ectotherm", "ectotherm", "ectotherm", "ect…
## $ locomotion <chr> "swimming", "swimming", "swimming", "swimmi…
## $ trophic.guild <chr> "carnivore", "carnivore", "carnivore", "car…
## $ dimension <dbl> 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3…
## $ preymass <dbl> NA, NA, NA, NA, NA, NA, 1.39, NA, NA, NA, N…
## $ log10.preymass <dbl> NA, NA, NA, NA, NA, NA, 0.1430148, NA, NA, …
## $ PPMR <dbl> NA, NA, NA, NA, NA, NA, 530, NA, NA, NA, NA…
## $ prey.size.reference <chr> NA, NA, NA, NA, NA, NA, "Brose U, et al. 20…names(homerange)## [1] "taxon" "common.name"
## [3] "class" "order"
## [5] "family" "genus"
## [7] "species" "primarymethod"
## [9] "N" "mean.mass.g"
## [11] "log10.mass" "alternative.mass.reference"
## [13] "mean.hra.m2" "log10.hra"
## [15] "hra.reference" "realm"
## [17] "thermoregulation" "locomotion"
## [19] "trophic.guild" "dimension"
## [21] "preymass" "log10.preymass"
## [23] "PPMR" "prey.size.reference"ggplot(data=homerange, #specify the data
mapping=aes(x=log10.mass, y=log10.hra))+ #map the aesthetics
geom_point() #add the plot type
In big data sets with lots of overlapping values, over-plotting can
be an issue. geom_jitter() is similar to
geom_point() but it helps with over plotting by adding some
random noise to the data and separating some of the individual
points.
ggplot(data=homerange, mapping=aes(x=log10.mass, y=log10.hra))+
geom_jitter() #if you have really big data set (over plotting) => adds some random noise
To add a regression (best of fit) line, we just add another layer.
ggplot(data=homerange, mapping=aes(x=log10.mass, y=log10.hra))+
geom_point()+
geom_smooth(method=lm, se=T) #add a regression line## `geom_smooth()` using formula = 'y ~ x'
life_history %>%
ggplot(aes(x=gestation, y=wean_mass))+
geom_jitter(na.rm = T) #prevents over plotting 
life_history %>%
select(gestation, wean_mass) %>%
ggplot(aes(x=gestation, y=wean_mass))+
geom_point(na.rm = T)+
scale_y_log10()
Examples What is the relationship between log10.hra and log10.preymass? What do you notice about how ggplot treats NA’s?
ggplot(data=homerange,
mapping = aes(x=log10.hra, y=log10.preymass))+
geom_point(na.rm=T)+
geom_smooth(method=lm, se=T, na.rm = T)## `geom_smooth()` using formula = 'y ~ x'
Bar Plot: geom_bar
The simplest type of bar plot counts the number of observations in a
categorical variable. In this case, we want to know how many
observations are present in the variable trophic.guild.
Notice that we do not specify a y-axis because it is count by
default.
names(homerange)## [1] "taxon" "common.name"
## [3] "class" "order"
## [5] "family" "genus"
## [7] "species" "primarymethod"
## [9] "N" "mean.mass.g"
## [11] "log10.mass" "alternative.mass.reference"
## [13] "mean.hra.m2" "log10.hra"
## [15] "hra.reference" "realm"
## [17] "thermoregulation" "locomotion"
## [19] "trophic.guild" "dimension"
## [21] "preymass" "log10.preymass"
## [23] "PPMR" "prey.size.reference"homerange %>%
count(trophic.guild)## # A tibble: 2 × 2
## trophic.guild n
## <chr> <int>
## 1 carnivore 342
## 2 herbivore 227Also notice that we can use pipes! The mapping= function
is implied by aes and so is often left out.
homerange %>%
ggplot(aes(trophic.guild)) + #don't need to use x since we are counting one variable
geom_bar() #good for counts and categorical data not continuous 
life_history <- read_csv("data/mammal_lifehistories_v2.csv", na="-999") %>% clean_names()life_history %>%
ggplot(aes(x=order))+ #counting observations in a catagorical variable
geom_bar()+
coord_flip()
Bar Plot: geom_col
Unlike geom_bar(), geom_col() allows us to
specify an x-axis and a y-axis.
homerange %>%
filter(family=="salmonidae") %>%
select(common.name, log10.mass) %>%
ggplot(aes(y=common.name, x=log10.mass))+ #notice the switch in x and y => specified the y first
geom_col()
geom_bar() with stat="identity"
stat="identity" allows us to map a variable to the y-axis
so that we aren’t restricted to counts.
homerange %>%
filter(family=="salmonidae") %>%
ggplot(aes(x=common.name, y=log10.mass))+
geom_bar(stat="identity") #only use when you want a count
life_history <- read_csv("data/mammal_lifehistories_v2.csv", na="-999") %>% clean_names()life_history %>%
count(order, sort = T) %>% #sort = T is the same as arrange
ggplot(aes(x=order, y=n))+
geom_col()+ #specify an x and y
coord_flip()
life_history %>%
group_by(order) %>%
summarise(mean_mass=mean(mass, na.rm = T)) %>%
ggplot(aes(x=order, y=mean_mass))+
geom_col()+
coord_flip()
There are a few problems here. First, the y-axis is in scientific notation. We can fix this by adjusting the options for the session.
options(scipen=999) #cancels scientific notation for the sessionNext, the y-axis is not on a log scale. We can fix this by adding
scale_y_log10().
life_history %>%
group_by(order) %>%
summarise(mean_mass=mean(mass, na.rm = T)) %>%
ggplot(aes(x=order, y=mean_mass))+
geom_col()+
coord_flip()+
scale_y_log10()
Lastly, we can adjust the x-axis labels to make them more readable.
We do this using reorder.
life_history %>%
group_by(order) %>%
summarise(mean_mass=mean(mass, na.rm = T)) %>%
ggplot(aes(x=reorder(order, mean_mass), y=mean_mass))+ #this orders x axis by mean mass
geom_col()+
coord_flip()+
scale_y_log10()
Examples Filter the homerange data to
include mammals only.
homerange %>%
filter(class=="mammalia")## # A tibble: 238 × 24
## taxon common.name class order family genus species primarymethod N
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 mammals giant golden mo… mamm… afro… chrys… chry… trevel… telemetry* <NA>
## 2 mammals Grant's golden … mamm… afro… chrys… erem… granti telemetry* <NA>
## 3 mammals pronghorn mamm… arti… antil… anti… americ… telemetry* <NA>
## 4 mammals impala mamm… arti… bovid… aepy… melamp… telemetry* <NA>
## 5 mammals hartebeest mamm… arti… bovid… alce… busela… telemetry* <NA>
## 6 mammals barbary sheep mamm… arti… bovid… ammo… lervia telemetry* <NA>
## 7 mammals American bison mamm… arti… bovid… bison bison telemetry* <NA>
## 8 mammals European bison mamm… arti… bovid… bison bonasus telemetry* <NA>
## 9 mammals goat mamm… arti… bovid… capra hircus telemetry* <NA>
## 10 mammals Spanish ibex mamm… arti… bovid… capra pyrena… telemetry* <NA>
## # ℹ 228 more rows
## # ℹ 15 more variables: mean.mass.g <dbl>, log10.mass <dbl>,
## # alternative.mass.reference <chr>, mean.hra.m2 <dbl>, log10.hra <dbl>,
## # hra.reference <chr>, realm <chr>, thermoregulation <chr>, locomotion <chr>,
## # trophic.guild <chr>, dimension <dbl>, preymass <dbl>, log10.preymass <dbl>,
## # PPMR <dbl>, prey.size.reference <chr>Are there more herbivores or carnivores in mammals? Make a bar plot that shows their relative numbers.
homerange %>%
select(class, trophic.guild) %>%
filter(class=="mammalia") %>%
ggplot(aes(trophic.guild))+
geom_bar()
Make a bar plot that shows the masses of the top 10 smallest mammals.
homerange %>%
filter(class=="mammalia") %>%
top_n(-10, log10.mass) %>%
ggplot(aes(x=common.name, y=log10.mass))+
geom_col()+ #use when you have x and y axis
coord_flip()
Examples
penguins## # A tibble: 344 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>In a previous lab, we asked how many penguins were measured on each island.
penguins %>%
count(island)## # A tibble: 3 × 2
## island n
## <fct> <int>
## 1 Biscoe 168
## 2 Dream 124
## 3 Torgersen 52Make this output more visual by adding a plot…
penguins %>%
count(island) %>%
ggplot(aes(x=island, y=n))+
geom_col()
names(penguins)## [1] "species" "island" "bill_length_mm"
## [4] "bill_depth_mm" "flipper_length_mm" "body_mass_g"
## [7] "sex" "year"What if we wanted a plot that showed the number of measured penguins for each species?
penguins %>%
count(species) %>%
ggplot(aes(x=species, y=n))+
geom_col()
penguins %>%
ggplot(aes(species))+
geom_bar()
How about average bill length by sex?
penguins %>%
filter(sex!="NA") %>%
group_by(sex) %>%
summarise(avg_bill_lenth_mm=mean(bill_length_mm)) %>%
ggplot(aes(x=sex, y=avg_bill_lenth_mm))+
geom_col()
Box Plots
For the next series of examples, we will use the
homerange data.
homerange <- read_csv("data/Tamburelloetal_HomeRangeDatabase.csv")Boxplots help us visualize a range of values. So, on the x-axis we
typically have something categorical and the y-axis is the range. In the
case below, we are plotting log10.mass by taxonomic class
in the homerange data. geom_boxplot() is the
geom type for a standard box plot. The center line in each box
represents the median, not the mean.
Let’s look at the variable log10.mass grouped by
taxonomic class.
homerange %>%
group_by(class) %>%
summarize(min_log10.mass=min(log10.mass),
max_log10.mass=max(log10.mass),
median_log10.mass=median(log10.mass))## # A tibble: 4 × 4
## class min_log10.mass max_log10.mass median_log10.mass
## <chr> <dbl> <dbl> <dbl>
## 1 actinopterygii -0.658 3.55 2.08
## 2 aves 0.712 4.95 1.82
## 3 mammalia 0.620 6.60 3.33
## 4 reptilia 0.539 4.03 2.51homerange %>%
ggplot(aes(x = class, y = log10.mass)) +
geom_boxplot()
Examples There are more herbivores than carnivores
in the homerange data, but how do their masses compare? Make a summary
and boxplot that compares their masses. Use log10.mass.
homerange %>%
select(trophic.guild, log10.mass) %>%
ggplot(aes(x=trophic.guild, y=log10.mass))+
geom_boxplot()
Now use a boxplot to visualize the range of log10.mass by family of mammalian carnivore.
homerange %>%
filter(trophic.guild=="carnivore" & class=="mammalia") %>%
ggplot(aes(x=family, y=log10.mass))+
geom_boxplot()+
coord_flip()
life_history %>%
ggplot(aes(x=order, y=mass))+
geom_boxplot(na.rm = T)+
coord_flip()+
scale_y_log10()
LAB 11
Aesthetics: Labels
elephants <- read_csv("data/elephantsMF.csv") %>% clean_names()## Rows: 288 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Sex
## dbl (2): Age, Height
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Make a plot that compares age and height of elephants.
elephants %>%
ggplot(aes(x=age, y=height))+
geom_point()+ #compares two continuous variables
geom_smooth(method = lm, se=F)## `geom_smooth()` using formula = 'y ~ x'
The plot looks clean, but it is incomplete. A reader unfamiliar with
the data might have a difficult time interpreting the labels. To add
custom labels, we use the labs command.
elephants %>%
ggplot(aes(x=age, y=height)) +
geom_point() +
geom_smooth(method=lm, se=F)+
labs(title="Elephant Age vs. Height", #adds a title
x="Age (years)", #adds names on axis
y="Height (cm)")## `geom_smooth()` using formula = 'y ~ x'
We can improve the plot further by adjusting the size and face of the
text. We do this using theme(). The rel()
option changes the relative size of the title to keep things consistent.
Adding hjust allows control of title position.
elephants %>%
ggplot(aes(x=age, y=height)) +
geom_point() +
geom_smooth(method=lm, se=F)+
labs(title="Elephant Age vs. Height", #adds a title
x="Age (years)", #adds names on axis
y="Height (cm)")+
theme(plot.title = element_text(size=rel(1.5), hjust=0.5)) #size changes the size of the title, hjust changes the location of the title, 0=left, 0.5=middle, 1=right## `geom_smooth()` using formula = 'y ~ x'
Other Aesthetics
There are lots of options for aesthetics. An aesthetic can be
assigned to either numeric or categorical data. fill is a
common grouping option; notice that an appropriate key is displayed when
you use one of these options.
elephants %>%
ggplot(aes(x=sex, fill=sex))+ #fill is a grouping option, color in bar by sex
geom_bar()
size adjusts the size of points relative to a continuous
variable.
life_history %>%
ggplot(aes(x=gestation, y=log10(mass), size=mass))+ #can adjust the size of point relative to the mass
geom_point(na.rm=T)
homerange <-
read_csv("data/Tamburelloetal_HomeRangeDatabase.csv", na = c("", "NA", "\\"))## Rows: 569 Columns: 24
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): taxon, common.name, class, order, family, genus, species, primarym...
## dbl (8): mean.mass.g, log10.mass, mean.hra.m2, log10.hra, dimension, preyma...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.There are many options to create nice plots in ggplot. One useful
trick is to store the plot as a new object and then experiment with
geom’s and aesthetics. Let’s setup a plot that compares
log10.mass and log10.hra. Notice that we are
not specifying a geom.
p <- homerange %>%
ggplot(aes(x= log10.mass, y= log10.hra))Play with point size by adjusting the size argument.
p + geom_point(size=0.25) #adjust the size of the dots
We can color the points by a categorical variable.
p + geom_point(aes(color=thermoregulation), size=1.00) #for scatter plots, color is the same as fill in bar plots
We can also map shapes to another categorical variable.
p + geom_point(aes(color=thermoregulation, shape=thermoregulation), size=2.00)
Barplots and multiple variables
At this point you should be comfortable building bar plots that show
counts of observations using geom_bar(). Last time we
explored the fill option as a way to bring color to the
plot; i.e. we filled by the same variable that we were plotting. What
happens when we fill by a different categorical variable?
Let’s start by counting how many observations we have in each taxonomic
group.
homerange %>% count(taxon, sort=T)## # A tibble: 9 × 2
## taxon n
## <chr> <int>
## 1 mammals 238
## 2 birds 140
## 3 marine fishes 90
## 4 snakes 41
## 5 river fishes 14
## 6 turtles 14
## 7 tortoises 12
## 8 lizards 11
## 9 lake fishes 9Now let’s make a bar plot of these data.
homerange %>%
count(taxon, sort=T) %>%
ggplot(aes(x=taxon, y=n))+
geom_col()+
coord_flip()+
labs(title = "Observation by Taxon",
x="Taxonomic group")
By specifying fill=trophic.guild we build a stacked bar
plot that shows the proportion of a given taxonomic group that is an
herbivore or carnivore.
homerange %>%
ggplot(aes(x=taxon, fill=trophic.guild))+
geom_bar()+
coord_flip()+
labs(title = "Observation by Taxon",
x="Taxonomic group")
homerange %>%
ggplot(aes(x=taxon, fill=taxon))+
geom_bar()+
coord_flip()+
labs(title = "Observation by Taxon",
x="Taxonomic group")
We can also have counts of each trophic guild within taxonomic group
shown side-by-side by specifying position="dodge".
homerange %>%
ggplot(aes(x=taxon, fill=trophic.guild))+
geom_bar(position = "dodge")+
coord_flip()+
labs(title = "Observation by Taxon",
x="Taxonomic group")
Here is the same plot oriented vertically.
homerange %>%
ggplot(aes(x=taxon, fill=trophic.guild))+
geom_bar(position = "dodge")+
theme(axis.text.x=element_text(angle=45, hjust = 1))+
labs(title = "Observation by Taxon",
x="Taxonomic group")
We can also scale all bars to a percentage.
homerange %>%
ggplot(aes(x = taxon, fill = trophic.guild))+
geom_bar(position = position_fill())+
scale_y_continuous(labels = scales::percent)+
coord_flip()
Examples Make a bar plot that shows counts of
individuals by island. Fill by species, and try both a stacked bar plot
and position="dodge".
penguins %>%
ggplot(aes(x=island, fill=species))+
geom_bar()+
labs(title = "Count of Individuals per Island",
x="Species",
y="Count")
penguins %>%
ggplot(aes(x=island, fill=species))+
geom_bar(position = "dodge")+
labs(title = "Count of Individuals per Island",
x="Species",
y="Count")
penguins %>%
count(species)## # A tibble: 3 × 2
## species n
## <fct> <int>
## 1 Adelie 152
## 2 Chinstrap 68
## 3 Gentoo 124penguins %>%
count(species) %>%
ggplot(aes(x=species, y=n))+
geom_col()+
labs(title = "Number of Species per Island",
x="Species",
y="Count")
penguins %>%
ggplot(aes(x=species, fill=species))+
geom_bar(position = "dodge")+
theme(axis.text.x=element_text(angle=45, hjust = 1))+
labs(title = "Number of Species per Island",
x="Species",
y="Count")
penguins %>%
ggplot(aes(x=species, fill=species))+
geom_bar(position = position_fill())+
theme(axis.text.x=element_text(angle=45, hjust = 1))+
labs(title = "Number of Species per Island",
x="Species",
y="Count")
penguins## # A tibble: 344 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>Make another bar plot that shows the number of individuals by sex on each island?
penguins %>%
count(sex)## # A tibble: 3 × 2
## sex n
## <fct> <int>
## 1 female 165
## 2 male 168
## 3 <NA> 11penguins %>%
filter(sex!="NA") %>%
ggplot(aes(x=island, fill=sex))+
geom_bar(position = "dodge")+
labs(title = "Number of Males and Females per Island",
x="Sex",
y="Count")
group
In addition to fill, group is an aesthetic
that accomplishes the same function but does not add color.
Here is a box plot that shows log10.mass by taxonomic
class.
homerange %>%
ggplot(aes(x = class, y = log10.mass)) +
geom_boxplot() #use for range
I use group to make individual box plots for each taxon
within class.
homerange %>%
ggplot(aes(x = class, y = log10.mass, group = taxon)) + #group makes individual box plots
geom_boxplot()
I can also use fill to associate the different taxa with
a color coded key.
homerange %>%
ggplot(aes(x = class, y = log10.mass, fill = taxon)) +
geom_boxplot(alpha=0.5) #adds transparency 
LAB 12
deserts <- read_csv("data/surveys_complete.csv")homerange <- read_csv("data/Tamburelloetal_HomeRangeDatabase.csv", na = c("", "NA", "\\"))Line plots ON MIDTERM 2
Line plots are great when you need to show changes over time. Here we
look at the number of samples for species DM and DS over the years
represented in the deserts data. This takes some careful
thought- we want to know how sampling has changed over time for these
two species.
Let’s start by making a clear x and y so we know what we are going to plot.
deserts %>%
filter(species_id=="DM" | species_id=="DS") %>%
mutate(year=as.factor(year)) %>% #change to factor because this represents data points
group_by(year, species_id) %>%
summarise(n=n(), .groups = "keep") %>%
pivot_wider(names_from = species_id, values_from = n)## # A tibble: 26 × 3
## # Groups: year [26]
## year DM DS
## <fct> <int> <int>
## 1 1977 264 98
## 2 1978 389 320
## 3 1979 209 204
## 4 1980 493 346
## 5 1981 559 354
## 6 1982 609 354
## 7 1983 528 280
## 8 1984 396 76
## 9 1985 667 98
## 10 1986 406 88
## # ℹ 16 more rowsdeserts %>%
filter(species_id=="DM" | species_id=="DS") %>%
mutate(year=as.factor(year)) %>% #change to factor because this represents data points
group_by(year, species_id) %>%
summarise(n=n(), .groups = "keep") %>%
ggplot(aes(x=year, y=n, group=species_id, color=species_id))+
geom_line()+
geom_point(shape=10)+ #can experiement with shapes
theme(axis.text.x = element_text(angle=60, hjust=1))+
labs(title="Number of Samples for Species DM and DS",
x="Year",
y="n")
Histograms
Histograms are frequently used by biologists; they show the
distribution of continuous variables. As students, you have seen
histograms of grade distributions. A histogram bins the
data and you specify the number of bins that encompass a range of
observations. For something like grades, this is easy because the number
of bins corresponds to the grades A-F. By default, R uses a formula to
calculate the number of bins but some adjustment may be required.
What does the distribution of body mass look like in the
homerange data?
homerange %>%
ggplot(aes(x = log10.mass)) +
geom_histogram(bins = 20)+ #we can adjust the number of bins with the bins argument
labs(title = "Distribution of Body Mass")
Let’s play with the colors. This shows all 657 of R’s built-in
colors. Notice that color and fill do
different things!
#grDevices::colors()Let’s rebuild the histogram, but this time we will specify the color and fill. Do a little experimentation on your own with the different colors.
homerange %>%
ggplot(aes(x = log10.mass)) +
geom_histogram(color = "black", fill = "papayawhip", bins=10)+
labs(title = "Distribution of Body Mass")
Density plots
Density plots are similar to histograms but they use a smoothing function to make the distribution more even and clean looking. They do not use bins.
homerange %>%
ggplot(aes(x = log10.mass)) +
geom_density(fill="deepskyblue4", alpha =0.4, color = "black")+ #alpha is the transparency, show continuous variables
labs(title = "Distribution of Body Mass")
I like to see both the histogram and the density curve so I often plot them together. Note that I assign the density plot a different color.
homerange %>%
ggplot(aes(x=log10.mass)) +
geom_histogram(aes(y = after_stat(density)), fill = "deepskyblue4", alpha = 0.4, color = "black")+
geom_density(color = "red")+
labs(title = "Distribution of Body Mass")
Examples 1. Make a histogram of
log10.hra. Make sure to add a title.
homerange %>%
ggplot(aes(x=log10.hra)) + #only need to specify x
geom_histogram(color = "black", fill = "salmon")+
labs(title = "Histogram of Homerange")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- Now plot the same variable using
geom_density().
homerange %>%
ggplot(aes(x=log10.hra)) +
geom_density(fill = "salmon", alpha=0.6, color = "black")+
labs(title = "Histogram of Homerange")
- Combine them both!
homerange %>%
ggplot(aes(x=log10.hra))+
geom_histogram(aes(y=after_stat(density)), fill="violetred4", alpha=0.3, color="black")+
geom_density(color="salmon")+
labs(title = "Histogram of Homerange")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Create Categories with mutate and case_when() ON MIDTERM 2
case_when() is a very handy function from
dplyr which allows us to calculate a new variable from
other variables. We use case_when() within
mutate() to do this.case_when() allows us to
specify multiple conditions. Let’s reclassify the body mass variable
into a new factor variable with small, medium, and large animals. In
this case, we are making a continuous variable into a categorical
variable.
glimpse(homerange)## Rows: 569
## Columns: 24
## $ taxon <chr> "lake fishes", "river fishes", "river fishe…
## $ common.name <chr> "american eel", "blacktail redhorse", "cent…
## $ class <chr> "actinopterygii", "actinopterygii", "actino…
## $ order <chr> "anguilliformes", "cypriniformes", "cyprini…
## $ family <chr> "anguillidae", "catostomidae", "cyprinidae"…
## $ genus <chr> "anguilla", "moxostoma", "campostoma", "cli…
## $ species <chr> "rostrata", "poecilura", "anomalum", "fundu…
## $ primarymethod <chr> "telemetry", "mark-recapture", "mark-recapt…
## $ N <chr> "16", NA, "20", "26", "17", "5", "2", "2", …
## $ mean.mass.g <dbl> 887.00, 562.00, 34.00, 4.00, 4.00, 3525.00,…
## $ log10.mass <dbl> 2.9479236, 2.7497363, 1.5314789, 0.6020600,…
## $ alternative.mass.reference <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ mean.hra.m2 <dbl> 282750.00, 282.10, 116.11, 125.50, 87.10, 3…
## $ log10.hra <dbl> 5.4514026, 2.4504031, 2.0648696, 2.0986437,…
## $ hra.reference <chr> "Minns, C. K. 1995. Allometry of home range…
## $ realm <chr> "aquatic", "aquatic", "aquatic", "aquatic",…
## $ thermoregulation <chr> "ectotherm", "ectotherm", "ectotherm", "ect…
## $ locomotion <chr> "swimming", "swimming", "swimming", "swimmi…
## $ trophic.guild <chr> "carnivore", "carnivore", "carnivore", "car…
## $ dimension <dbl> 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3…
## $ preymass <dbl> NA, NA, NA, NA, NA, NA, 1.39, NA, NA, NA, N…
## $ log10.preymass <dbl> NA, NA, NA, NA, NA, NA, 0.1430148, NA, NA, …
## $ PPMR <dbl> NA, NA, NA, NA, NA, NA, 530, NA, NA, NA, NA…
## $ prey.size.reference <chr> NA, NA, NA, NA, NA, NA, "Brose U, et al. 20…homerange %>%
select(log10.mass) %>%
summarize(min=min(log10.mass),
max=max(log10.mass))## # A tibble: 1 × 2
## min max
## <dbl> <dbl>
## 1 -0.658 6.60summary(homerange$log10.mass)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.6576 1.6990 2.5185 2.5947 3.3324 6.6021homerange %>% #ON MIDTERM 2
mutate(mass_category=case_when(log10.mass<=1.75 ~ "small",
log10.mass>1.75 & log10.mass<=2.75 ~ "medium",
log10.mass>2.75 ~ "large")) %>%
select(mass_category)## # A tibble: 569 × 1
## mass_category
## <chr>
## 1 large
## 2 medium
## 3 small
## 4 small
## 5 small
## 6 large
## 7 large
## 8 medium
## 9 medium
## 10 large
## # ℹ 559 more rowsHere we check how the newly created body mass categories compare
across trophic.guild.
homerange %>% #ON MIDTERM 2
mutate(mass_category=case_when(log10.mass<=1.75 ~ "small",
log10.mass>1.75 & log10.mass<=2.75 ~ "medium",
log10.mass>2.75 ~ "large")) %>%
ggplot(aes(x=mass_category, fill=trophic.guild))+
geom_bar(position = "dodge")
Examples 1. Use case_when() to make a new column
range_category that breaks down log10.hra into
very small, small, medium, and large classes based on quartile.
library(gtools)
#install.packages("gtools")summary(homerange$log10.mass)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.6576 1.6990 2.5185 2.5947 3.3324 6.6021quartiles <- quantcut(homerange$log10.hra)
table(quartiles)## quartiles
## [-1.52,3.65] (3.65,4.59] (4.59,6.02] (6.02,9.55]
## 143 142 142 142homerange %>%
mutate(range_category=case_when(log10.hra<3.65 ~"very small",
log10.hra>=3.65 & log10.hra<=4.59 ~ "small",
log10.hra>4.59 & log10.hra<=6.02 ~ "medium",
log10.hra>=6.02 ~ "large"))## # A tibble: 569 × 25
## taxon common.name class order family genus species primarymethod N
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 lake fishes american e… acti… angu… angui… angu… rostra… telemetry 16
## 2 river fishes blacktail … acti… cypr… catos… moxo… poecil… mark-recaptu… <NA>
## 3 river fishes central st… acti… cypr… cypri… camp… anomal… mark-recaptu… 20
## 4 river fishes rosyside d… acti… cypr… cypri… clin… fundul… mark-recaptu… 26
## 5 river fishes longnose d… acti… cypr… cypri… rhin… catara… mark-recaptu… 17
## 6 river fishes muskellunge acti… esoc… esoci… esox masqui… telemetry 5
## 7 marine fish… pollack acti… gadi… gadid… poll… pollac… telemetry 2
## 8 marine fish… saithe acti… gadi… gadid… poll… virens telemetry 2
## 9 marine fish… lined surg… acti… perc… acant… acan… lineat… direct obser… <NA>
## 10 marine fish… orangespin… acti… perc… acant… naso litura… telemetry 8
## # ℹ 559 more rows
## # ℹ 16 more variables: mean.mass.g <dbl>, log10.mass <dbl>,
## # alternative.mass.reference <chr>, mean.hra.m2 <dbl>, log10.hra <dbl>,
## # hra.reference <chr>, realm <chr>, thermoregulation <chr>, locomotion <chr>,
## # trophic.guild <chr>, dimension <dbl>, preymass <dbl>, log10.preymass <dbl>,
## # PPMR <dbl>, prey.size.reference <chr>, range_category <chr>- Make a plot that shows how many and which taxonomic classes are
represented in each
range_category.
homerange %>%
mutate(range_category=case_when(log10.hra<3.65 ~"very small",
log10.hra>=3.65 & log10.hra<=4.59 ~ "small",
log10.hra>4.59 & log10.hra<=6.02 ~ "medium",
log10.hra>=6.02 ~ "large")) %>%
ggplot(aes(x=range_category, fill=class))+
geom_bar(position = "dodge", alpha=0.6, color="black")
- Isolate the small
range_categoryand plot the range oflog10.massby taxonomic class.
homerange %>%
mutate(range_category=case_when(log10.hra<3.65 ~"very small",
log10.hra>=3.65 & log10.hra<=4.59 ~ "small",
log10.hra>4.59 & log10.hra<=6.02 ~ "medium",
log10.hra>=6.02 ~ "large")) %>%
filter(range_category=="small") %>%
ggplot(aes(x=class, y=log10.mass, fill=class))+
geom_boxplot()
ggplot themes
There are many options to change the theme of your plots within ggplot. Have a look [here]https://ggplot2.tidyverse.org/reference/ggtheme.html) for a list of the themes.
Let’s start by building a simple barplot.
p <- homerange %>%
ggplot(aes(x=taxon, fill=trophic.guild))+
geom_bar(na.rm=T, position="dodge")
p
Have a look at the linedraw theme; I am adding it as
another layer.
p + theme_linedraw()+
theme(axis.text.x = element_text(angle = 60, hjust=1))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
Examples 1. Build a scatterplot that compares
log10.mass and log10.preymass. Color the
points by taxon. Store this plot as object
q.
q <- homerange %>%
ggplot(aes(x=log10.mass, y=log10.preymass, color=taxon))+
geom_point(na.rm = T)
q
- Use plot
qbut add the classic theme.
q + theme_classic()
q + theme_minimal()
Legends
There are lots of options to manipulate legends. Have a look here.
p+theme_linedraw()+
theme(legend.position = "left", #moves the legend to differ location
axis.text.x = element_text(angle = 60, hjust=1))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL, #removes label from x axis
y= "n",
fill= "Trophic Guild")
ggthemes
There are many packages that include additional themes, one of which is ggthemes. Some of these are nice because they are designed to mimic the look of popular publications.
#install.packages("ggthemes")
library(ggthemes)Here is a list of the ggthemes
#ls("package:ggthemes")[grepl("theme_", ls("package:ggthemes"))]p +
theme_fivethirtyeight()+
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 60, hjust=1))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
Example 1. Use plot q and try the
theme_economist() theme.
p +
theme_economist()+
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 60, hjust=0))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
RColorBrewer
The default colors used by ggplot are often uninspiring. They don’t make plots pop out in presentations or publications, and you may want to use a customized palette to make things visually consistent.
Access the help for RColorBrewer.
?RColorBrewer #There are 3 types of palettes, sequential, diverging, and qualitative- Sequential palettes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values.
- Diverging palettes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues.
- Qualitative palettes do not imply magnitude differences between
legend classes, and hues are used to create the primary visual
differences between classes. Qualitative schemes are best suited to
representing nominal or categorical data. The thing to notice is that
there are three different color palettes: 1) sequential, 2) diverging,
and 3) qualitative. Within each of these there are several selections.
You can bring up the colors by using
display.brewer.pal(). Specify the number of colors that you want and the palette name.
display.brewer.pal(9,"BuPu") #sequential palette
The R Color Brewer website is very helpful for getting an idea of the color palettes. To make things easy, use these two guidelines:
+scale_colour_brewer() is for points
+scale_fill_brewer() is for fills
Here I chose the Paired palette. Take a moment and
experiment with other options.
p+scale_fill_brewer(palette = "Paired")+
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 60, hjust=1))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
Example 1. Keep using the sameq` plot,
but test out one of R Color Brewer palettes.
q + scale_colour_brewer(palette = "RdYlBu")+
theme_solarized()+
theme(legend.position = "left",
axis.text.x = element_text(angle = 60, hjust=0))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
Manually Setting Colors
You can also use paleteer to build a custom palette for
consistency. To access the paleteer collection, I add it to
a new object.
colors <- paletteer::palettes_d_namescolors## # A tibble: 2,415 × 5
## package palette length type novelty
## <chr> <chr> <int> <chr> <lgl>
## 1 awtools a_palette 8 sequential TRUE
## 2 awtools ppalette 8 qualitative TRUE
## 3 awtools bpalette 16 qualitative TRUE
## 4 awtools gpalette 4 sequential TRUE
## 5 awtools mpalette 9 qualitative TRUE
## 6 awtools spalette 6 qualitative TRUE
## 7 basetheme brutal 10 qualitative TRUE
## 8 basetheme clean 10 qualitative TRUE
## 9 basetheme dark 10 qualitative TRUE
## 10 basetheme deepblue 10 qualitative TRUE
## # ℹ 2,405 more rowsNow we can display the palettes. Assign the palette to
my_palette and then build this base R bar plot. There are a
lot of options; paleteer is a collection of popular
palettes. I really like the [ggsci package] (https://cran.r-project.org/web/packages/ggsci/vignettes/ggsci.html)
my_palette <- paletteer_d("vangogh::SunflowersMunich") barplot(rep(1,6), axes=FALSE, col=my_palette)
Now we just identify my_palette as part of
scale_fill_manual()
p+scale_fill_manual(values=my_palette)+
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 60, hjust=1))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
Example 1. Keep using the
sameqplot, but test out one ofpaleteer`
themes.
my_palette2 <- paletteer_d("ggsci::springfield_simpsons") barplot(rep(1,6), axes=FALSE, col=my_palette2)
q+theme_classic()+scale_colour_manual(values=my_palette2)+
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 60, hjust=1))+
labs(title = "Observations by Taxon in Homerange Data",
x = NULL,
y= "n",
fill= "Trophic Guild")
Faceting
Faceting is one of the amazing features of ggplot. It allows us to make multi-panel plots for easy comparison. Here is a boxplot that shows the range of log10.mass by taxon.
homerange %>%
ggplot(aes(x=taxon, y=log10.mass))+
geom_boxplot()+
facet_wrap(~trophic.guild)+
theme(axis.text.x = element_text(angle = 60, hjust=1))
There are other categorical variables that might be interesting to
overlay. facet_wrap() makes a ribbon of panels by a
specified categorical variable and allows you to control how you want
them arranged.
homerange %>%
ggplot(aes(x=taxon, y=log10.mass))+
geom_boxplot()+
facet_wrap(~trophic.guild, ncol=2)+
theme(axis.text.x = element_text(angle = 60, hjust=1))
facet_grid() allows control over the faceted variable;
it can be arranged in rows or columns. rows~columns.
homerange %>%
ggplot(aes(x=taxon, y=log10.mass))+
geom_boxplot()+
facet_grid(trophic.guild~.)+
theme(axis.text.x = element_text(angle = 60, hjust=1))
facet_grid() will also allow the comparison of two
categorical variables, just remember a~b where a is rows and b is
columns.
homerange %>%
ggplot(aes(x=taxon, y=log10.mass))+
geom_boxplot()+
facet_grid(trophic.guild~thermoregulation)+
theme(axis.text.x = element_text(angle = 60, hjust=1))
Examples 1. Use facet_wrap to compare the
distribution of log10.mass by
thermoregulation.
homerange %>%
ggplot(aes(x=log10.mass))+
geom_density(fill="steelblue", alpha=0.3)+
facet_wrap(~thermoregulation)
- Use facet_grid to compare the distribution of
log10.massbylocomotion.
homerange %>%
ggplot(aes(x=log10.hra))+
geom_density(fill="steelblue", alpha=0.3)+
facet_grid(.~locomotion)